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The setup script is the centre of all activity in building, distributing, and
installing modules using the Distutils. The main purpose of the setup script is
to describe your module distribution to the Distutils, so that the various
commands that operate on your modules do the right thing. As we saw in section
A Simple Example above, the setup script consists mainly of a call to
setup(), and most information supplied to the Distutils by the module
developer is supplied as keyword arguments to setup().

Here’s a slightly more involved example, which we’ll follow for the next couple
of sections: the Distutils’ own setup script. (Keep in mind that although the
Distutils are included with Python 1.6 and later, they also have an independent
existence so that Python 1.5.2 users can use them to install other module
distributions. The Distutils’ own setup script, shown here, is used to install
the package into Python 1.5.2.)

There are only two differences between this and the trivial one-file
distribution presented in section A Simple Example: more metadata, and the
specification of pure Python modules by package, rather than by module. This is
important since the Distutils consist of a couple of dozen modules split into
(so far) two packages; an explicit list of every module would be tedious to
generate and difficult to maintain. For more information on the additional
meta-data, see section Additional meta-data.

Note that any pathnames (files or directories) supplied in the setup script
should be written using the Unix convention, i.e. slash-separated. The
Distutils will take care of converting this platform-neutral representation into
whatever is appropriate on your current platform before actually using the
pathname. This makes your setup script portable across operating systems, which
of course is one of the major goals of the Distutils. In this spirit, all
pathnames in this document are slash-separated.

This, of course, only applies to pathnames given to Distutils functions. If
you, for example, use standard Python functions such as glob.glob() or
os.listdir() to specify files, you should be careful to write portable
code instead of hardcoding path separators:

The packages option tells the Distutils to process (build, distribute,
install, etc.) all pure Python modules found in each package mentioned in the
packages list. In order to do this, of course, there has to be a
correspondence between package names and directories in the filesystem. The
default correspondence is the most obvious one, i.e. package distutils is
found in the directory distutils relative to the distribution root.
Thus, when you say packages=['foo'] in your setup script, you are
promising that the Distutils will find a file foo/__init__.py (which
might be spelled differently on your system, but you get the idea) relative to
the directory where your setup script lives. If you break this promise, the
Distutils will issue a warning but still process the broken package anyways.

If you use a different convention to lay out your source directory, that’s no
problem: you just have to supply the package_dir option to tell the
Distutils about your convention. For example, say you keep all Python source
under lib, so that modules in the “root package” (i.e., not in any
package at all) are in lib, modules in the foo package are in
lib/foo, and so forth. Then you would put

package_dir={'':'lib'}

in your setup script. The keys to this dictionary are package names, and an
empty package name stands for the root package. The values are directory names
relative to your distribution root. In this case, when you say packages=['foo'], you are promising that the file lib/foo/__init__.py exists.

Another possible convention is to put the foo package right in
lib, the foo.bar package in lib/bar, etc. This would be
written in the setup script as

package_dir={'foo':'lib'}

A package:dir entry in the package_dir dictionary implicitly
applies to all packages below package, so the foo.bar case is
automatically handled here. In this example, having packages=['foo','foo.bar'] tells the Distutils to look for lib/__init__.py and
lib/bar/__init__.py. (Keep in mind that although package_dir
applies recursively, you must explicitly list all packages in
packages: the Distutils will not recursively scan your source tree
looking for any directory with an __init__.py file.)

For a small module distribution, you might prefer to list all modules rather
than listing packages—especially the case of a single module that goes in the
“root package” (i.e., no package at all). This simplest case was shown in
section A Simple Example; here is a slightly more involved example:

py_modules=['mod1','pkg.mod2']

This describes two modules, one of them in the “root” package, the other in the
pkg package. Again, the default package/directory layout implies that
these two modules can be found in mod1.py and pkg/mod2.py, and
that pkg/__init__.py exists as well. And again, you can override the
package/directory correspondence using the package_dir option.

Just as writing Python extension modules is a bit more complicated than writing
pure Python modules, describing them to the Distutils is a bit more complicated.
Unlike pure modules, it’s not enough just to list modules or packages and expect
the Distutils to go out and find the right files; you have to specify the
extension name, source file(s), and any compile/link requirements (include
directories, libraries to link with, etc.).

All of this is done through another keyword argument to setup(), the
ext_modules option. ext_modules is just a list of
Extension instances, each of which describes a single extension module.
Suppose your distribution includes a single extension, called foo and
implemented by foo.c. If no additional instructions to the
compiler/linker are needed, describing this extension is quite simple:

Extension('foo',['foo.c'])

The Extension class can be imported from distutils.core along
with setup(). Thus, the setup script for a module distribution that
contains only this one extension and nothing else might be:

The Extension class (actually, the underlying extension-building
machinery implemented by the build_ext command) supports a great deal
of flexibility in describing Python extensions, which is explained in the
following sections.

The first argument to the Extension constructor is always the name of
the extension, including any package names. For example,

Extension('foo',['src/foo1.c','src/foo2.c'])

describes an extension that lives in the root package, while

Extension('pkg.foo',['src/foo1.c','src/foo2.c'])

describes the same extension in the pkg package. The source files and
resulting object code are identical in both cases; the only difference is where
in the filesystem (and therefore where in Python’s namespace hierarchy) the
resulting extension lives.

If you have a number of extensions all in the same package (or all under the
same base package), use the ext_package keyword argument to
setup(). For example,

The second argument to the Extension constructor is a list of source
files. Since the Distutils currently only support C, C++, and Objective-C
extensions, these are normally C/C++/Objective-C source files. (Be sure to use
appropriate extensions to distinguish C++source files: .cc and
.cpp seem to be recognized by both Unix and Windows compilers.)

However, you can also include SWIG interface (.i) files in the list; the
build_ext command knows how to deal with SWIG extensions: it will run
SWIG on the interface file and compile the resulting C/C++ file into your
extension.

This warning notwithstanding, options to SWIG can be currently passed like
this:

On some platforms, you can include non-source files that are processed by the
compiler and included in your extension. Currently, this just means Windows
message text (.mc) files and resource definition (.rc) files for
Visual C++. These will be compiled to binary resource (.res) files and
linked into the executable.

Three optional arguments to Extension will help if you need to specify
include directories to search or preprocessor macros to define/undefine:
include_dirs, define_macros, and undef_macros.

For example, if your extension requires header files in the include
directory under your distribution root, use the include_dirs option:

Extension('foo',['foo.c'],include_dirs=['include'])

You can specify absolute directories there; if you know that your extension will
only be built on Unix systems with X11R6 installed to /usr, you can get
away with

Extension('foo',['foo.c'],include_dirs=['/usr/include/X11'])

You should avoid this sort of non-portable usage if you plan to distribute your
code: it’s probably better to write C code like

#include <X11/Xlib.h>

If you need to include header files from some other Python extension, you can
take advantage of the fact that header files are installed in a consistent way
by the Distutils install_header command. For example, the Numerical
Python header files are installed (on a standard Unix installation) to
/usr/local/include/python1.5/Numerical. (The exact location will differ
according to your platform and Python installation.) Since the Python include
directory—/usr/local/include/python1.5 in this case—is always
included in the search path when building Python extensions, the best approach
is to write C code like

#include <Numerical/arrayobject.h>

If you must put the Numerical include directory right into your header
search path, though, you can find that directory using the Distutils
distutils.sysconfig module:

Even though this is quite portable—it will work on any Python installation,
regardless of platform—it’s probably easier to just write your C code in the
sensible way.

You can define and undefine pre-processor macros with the define_macros and
undef_macros options. define_macros takes a list of (name,value)
tuples, where name is the name of the macro to define (a string) and
value is its value: either a string or None. (Defining a macro FOO
to None is the equivalent of a bare #defineFOO in your C source: with
most compilers, this sets FOO to the string 1.) undef_macros is
just a list of macros to undefine.

You can also specify the libraries to link against when building your extension,
and the directories to search for those libraries. The libraries option is
a list of libraries to link against, library_dirs is a list of directories
to search for libraries at link-time, and runtime_library_dirs is a list of
directories to search for shared (dynamically loaded) libraries at run-time.

For example, if you need to link against libraries known to be in the standard
library search path on target systems

Extension(...,libraries=['gdbm','readline'])

If you need to link with libraries in a non-standard location, you’ll have to
include the location in library_dirs:

Extension(...,library_dirs=['/usr/X11R6/lib'],libraries=['X11','Xt'])

(Again, this sort of non-portable construct should be avoided if you intend to
distribute your code.)

There are still some other options which can be used to handle special cases.

The extra_objects option is a list of object files to be passed to the
linker. These files must not have extensions, as the default extension for the
compiler is used.

extra_compile_args and extra_link_args can be used to
specify additional command line options for the respective compiler and linker
command lines.

export_symbols is only useful on Windows. It can contain a list of
symbols (functions or variables) to be exported. This option is not needed when
building compiled extensions: Distutils will automatically add initmodule
to the list of exported symbols.

Dependencies on other Python modules and packages can be specified by supplying
the requires keyword argument to setup(). The value must be a list of
strings. Each string specifies a package that is required, and optionally what
versions are sufficient.

To specify that any version of a module or package is required, the string
should consist entirely of the module or package name. Examples include
'mymodule' and 'xml.parsers.expat'.

If specific versions are required, a sequence of qualifiers can be supplied in
parentheses. Each qualifier may consist of a comparison operator and a version
number. The accepted comparison operators are:

< > ==
<= >= !=

These can be combined by using multiple qualifiers separated by commas (and
optional whitespace). In this case, all of the qualifiers must be matched; a
logical AND is used to combine the evaluations.

Let’s look at a bunch of examples:

Requires Expression

Explanation

==1.0

Only version 1.0 is compatible

>1.0,!=1.5.1,<2.0

Any version after 1.0 and before 2.0
is compatible, except 1.5.1

Now that we can specify dependencies, we also need to be able to specify what we
provide that other distributions can require. This is done using the provides
keyword argument to setup(). The value for this keyword is a list of
strings, each of which names a Python module or package, and optionally
identifies the version. If the version is not specified, it is assumed to match
that of the distribution.

Some examples:

Provides Expression

Explanation

mypkg

Provide mypkg, using the distribution
version

mypkg(1.1)

Provide mypkg version 1.1, regardless of
the distribution version

A package can declare that it obsoletes other packages using the obsoletes
keyword argument. The value for this is similar to that of the requires
keyword: a list of strings giving module or package specifiers. Each specifier
consists of a module or package name optionally followed by one or more version
qualifiers. Version qualifiers are given in parentheses after the module or
package name.

The versions identified by the qualifiers are those that are obsoleted by the
distribution being described. If no qualifiers are given, all versions of the
named module or package are understood to be obsoleted.

So far we have been dealing with pure and non-pure Python modules, which are
usually not run by themselves but imported by scripts.

Scripts are files containing Python source code, intended to be started from the
command line. Scripts don’t require Distutils to do anything very complicated.
The only clever feature is that if the first line of the script starts with
#! and contains the word “python”, the Distutils will adjust the first line
to refer to the current interpreter location. By default, it is replaced with
the current interpreter location. The --executable (or -e)
option will allow the interpreter path to be explicitly overridden.

The scripts option simply is a list of files to be handled in this
way. From the PyXML setup script:

Often, additional files need to be installed into a package. These files are
often data that’s closely related to the package’s implementation, or text files
containing documentation that might be of interest to programmers using the
package. These files are called package data.

Package data can be added to packages using the package_data keyword
argument to the setup() function. The value must be a mapping from
package name to a list of relative path names that should be copied into the
package. The paths are interpreted as relative to the directory containing the
package (information from the package_dir mapping is used if appropriate);
that is, the files are expected to be part of the package in the source
directories. They may contain glob patterns as well.

The path names may contain directory portions; any necessary directories will be
created in the installation.

For example, if a package should contain a subdirectory with several data files,
the files can be arranged like this in the source tree:

The data_files option can be used to specify additional files needed
by the module distribution: configuration files, message catalogs, data files,
anything which doesn’t fit in the previous categories.

data_files specifies a sequence of (directory, files) pairs in the
following way:

Note that you can specify the directory names where the data files will be
installed, but you cannot rename the data files themselves.

Each (directory, files) pair in the sequence specifies the installation
directory and the files to install there. If directory is a relative path, it
is interpreted relative to the installation prefix (Python’s sys.prefix for
pure-Python packages, sys.exec_prefix for packages that contain extension
modules). Each file name in files is interpreted relative to the
setup.py script at the top of the package source distribution. No
directory information from files is used to determine the final location of
the installed file; only the name of the file is used.

You can specify the data_files options as a simple sequence of files
without specifying a target directory, but this is not recommended, and the
install command will print a warning in this case. To install data
files directly in the target directory, an empty string should be given as the
directory.

The setup script may include additional meta-data beyond the name and version.
This information includes:

Meta-Data

Description

Value

Notes

name

name of the package

short string

(1)

version

version of this release

short string

(1)(2)

author

package author’s name

short string

(3)

author_email

email address of the
package author

email address

(3)

maintainer

package maintainer’s name

short string

(3)

maintainer_email

email address of the
package maintainer

email address

(3)

url

home page for the package

URL

(1)

description

short, summary
description of the
package

short string

long_description

longer description of the
package

long string

download_url

location where the
package may be downloaded

URL

(4)

classifiers

a list of classifiers

list of strings

(4)

platforms

a list of platforms

list of strings

license

license for the package

short string

(6)

Notes:

These fields are required.

It is recommended that versions take the form major.minor[.patch[.sub]].

Either the author or the maintainer must be identified.

These fields should not be used if your package is to be compatible with Python
versions prior to 2.2.3 or 2.3. The list is available from the PyPI website.

The license field is a text indicating the license covering the
package where the license is not a selection from the “License” Trove
classifiers. See the Classifier field. Notice that
there’s a licence distribution option which is deprecated but still
acts as an alias for license.

Encoding the version information is an art in itself. Python packages generally
adhere to the version format major.minor[.patch][sub]. The major number is 0
for initial, experimental releases of software. It is incremented for releases
that represent major milestones in a package. The minor number is incremented
when important new features are added to the package. The patch number
increments when bug-fix releases are made. Additional trailing version
information is sometimes used to indicate sub-releases. These are
“a1,a2,...,aN” (for alpha releases, where functionality and API may change),
“b1,b2,...,bN” (for beta releases, which only fix bugs) and “pr1,pr2,...,prN”
(for final pre-release release testing). Some examples:

If you wish to include classifiers in your setup.py file and also wish
to remain backwards-compatible with Python releases prior to 2.2.3, then you can
include the following code fragment in your setup.py before the
setup() call.

# patch distutils if it can't cope with the "classifiers" or# "download_url" keywordsfromsysimportversionifversion<'2.2.3':fromdistutils.distimportDistributionMetadataDistributionMetadata.classifiers=NoneDistributionMetadata.download_url=None

Sometimes things go wrong, and the setup script doesn’t do what the developer
wants.

Distutils catches any exceptions when running the setup script, and print a
simple error message before the script is terminated. The motivation for this
behaviour is to not confuse administrators who don’t know much about Python and
are trying to install a package. If they get a big long traceback from deep
inside the guts of Distutils, they may think the package or the Python
installation is broken because they don’t read all the way down to the bottom
and see that it’s a permission problem.

On the other hand, this doesn’t help the developer to find the cause of the
failure. For this purpose, the DISTUTILS_DEBUG environment variable can be set
to anything except an empty string, and distutils will now print detailed
information what it is doing, and prints the full traceback in case an exception
occurs.